Machine learning the thermodynamics of complex materials with ab initio accuracy

从头开始准确地机器学习复杂材料的热力学

• 批准号: 429582718
• 负责人: Professor Dr. Blazej Grabowski
• 金额: --
• 依托单位: Abteilung für Materialdesign
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/429582718?language=en
• 关键词: Machine; learning; thermodynamics; complex; materials

The general aim of the proposed project Mach-Initio is the ab initio investigation of the fundamental physical finite-temperature excitation mechanisms and their mutual coupling effects that determine the basic thermodynamic properties in complex materials. For that purpose, we leverage ideas and knowledge of the PIs from the field of machine learning and the field of ab initio materials design into a single and unique framework. The PIs have successfully collaborated in the recent past providing a solid basis for Mach-Initio. The combination of their expertise into a single joint project guarantees a successful fulfillment of the ambitious goals.Ab initio methods have been successfully applied for many years to calculate the zero Kelvin ground state energy of materials, however, direct ab initio calculations of excitations at finite temperatures are in most cases prohibitively expensive. Recent joint work of the PIs has shown that effective Hamiltonians based on machine-learning potentials, in particular moment tensor potentials (MTPs) and low-rank potentials (LRPs), can be utilized to reduce the computational effort drastically, thus enabling a highly efficient study of the vibrational and configurational excitations with ab initio accuracy. The aim of the present project is to further advance these finite-temperature ab initio approaches based on MTPs and LRPs. In particular, we will develop algorithms for the computation of a highly accurate free energy surface including all relevant excitation mechanisms related to vibrations, configurational entropy, magnetism, and their mutual coupling effects. To this end, a novel type of MTPs will be developed that accounts for the magnetic degrees of freedom (mMTPs). The conceptual and methodological framework of the mMTPs will be developed by the Russian side. The integration of the mMTPs into an ab initio-based thermodynamic methodology including the application to and validation for technologically relevant material systems will be pursued by the German side.

拟议项目 Mach-Initio 的总体目标是从头开始研究基本物理有限温度激发机制及其决定复杂材料基本热力学性质的相互耦合效应。为此，我们将机器学习领域和从头开始材料设计领域的 PI 想法和知识融入到一个单一且独特的框架中。最近，PI 成功合作，为 Mach-Initio 奠定了坚实的基础。将他们的专业知识结合到一个联合项目中，保证了雄心勃勃的目标的成功实现。多年来，从头计算方法已成功应用于计算材料的零开尔文基态能量，但是，在大多数情况下，在有限温度下直接从头计算激发的成本极其昂贵。 PI 最近的联合工作表明，基于机器学习势，特别是矩张量势 (MTP) 和低秩势 (LRP) 的有效哈密顿量，可用于大幅减少计算量，从而能够以从头算的精度对振动和构型激励进行高效研究。本项目的目的是进一步推进这些基于 MTP 和 LRP 的有限温度从头算方法。特别是，我们将开发用于计算高精度自由能表面的算法，包括与振动、构型熵、磁性及其相互耦合效应相关的所有相关激励机制。为此，我们将开发一种新型 MTP，用于解释磁自由度 (mMTP)。 mMTP的概念和方法框架将由俄罗斯方面制定。德国方面将致力于将 mMTP 整合到从头开始的热力学方法中，包括技术相关材料系统的应用和验证。